AI with Papers - Artificial Intelligence & Deep Learning
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All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/ #AI #chatGPT
Mostrar más📈 Análisis del canal de Telegram AI with Papers - Artificial Intelligence & Deep Learning
El canal AI with Papers - Artificial Intelligence & Deep Learning (@ai_deeplearning) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 17 261 suscriptores, ocupando la posición 7 500 en la categoría Tecnologías y Aplicaciones y el puesto 2 124 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 17 261 suscriptores.
Según los últimos datos del 24 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 258, y en las últimas 24 horas de -2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 16.76%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 7.11% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 893 visualizaciones. En el primer día suele acumular 1 227 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 16.
- Intereses temáticos: El contenido se centra en temas clave como framework, object, dataset, tba, depth.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“All the AI with papers. Every day fresh updates about #DeepLearning #MachineLearning #LLM & #ComputerVision
Curated by Alessandro Ferrari | https://www.linkedin.com/in/visionarynet/
#AI #chatGPT”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
17 261
Suscriptores
-224 horas
+27 días
+25830 días
Archivo de publicaciones
🎭 TRG: new SOTA in 6DoF Head 🎭
👉ECE (Korea) unveils TRG, a novel landmark-based method for estimating a 6DoF head pose which stands out for its explicit bidirectional interaction structure. Experiments on ARKitFace & BIWI confirm it's the new SOTA. Source Code & Models to be released💙
👉Review https://t.ly/lOIRA
👉Paper https://lnkd.in/dCWEwNyF
👉Code https://lnkd.in/dzRrwKBD
🧿 Shape of Motion for 4D 🧿
👉 Google (+Berkeley) unveils a novel method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion, from casually captured monocular videos. Impressive tracking capabilities. Source Code released 💙
👉Review https://t.ly/d9RsA
👉Project https://shape-of-motion.github.io/
👉Paper arxiv.org/pdf/2407.13764
👉Code github.com/vye16/shape-of-motion/
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📈Gradient Boosting Reinforcement Learning📈
👉#Nvidia unveils GBRL, a framework that extends the advantages of Gradient Boosting Trees to the RL domain. GBRL adapts the power of Gradient Boosting Trees to the unique challenges of RL environments, including non-stationarity and absence of predefined targets. Code released💙
👉Review https://t.ly/zv9pl
👉Paper https://arxiv.org/pdf/2407.08250
👉Code https://github.com/NVlabs/gbrl
💌 KineTy: Typography Diffusion 💌
👉GIST introduces a novel realistic kinetic typography generation driven by text description. Guided video diffusion models to achieve visually-pleasing text appearances. Repo to be released under Attribution-NC 4.0💙
👉Review https://t.ly/2FWo9
👉Paper arxiv.org/pdf/2407.10476
👉Project seonmip.github.io/kinety/
👉Repo github.com/SeonmiP/KineTy/tree/main
💌 KineTy: Typography Diffusion 💌
👉GIST introduces a novel realistic kinetic typography generation driven by text description. Guided video diffusion models to achieve visually-pleasing text appearances. Repo to be released under Attribution-NC 4.0💙
👉Review https://t.ly/2FWo9
👉Paper arxiv.org/pdf/2407.10476
👉Project seonmip.github.io/kinety/
👉Repo github.com/SeonmiP/KineTy/tree/main
🥥 OmniNOCS: largest 3D NOCS 🥥
👉OmniNOCS by #Google (+Georgia) is a unified NOCS (Normalized Object Coordinate Space) dataset that contains data across different domains with 90+ object classes. The largest NOCS dataset to date. Data & Code available under Apache 2.0💙
👉Review https://t.ly/xPgBn
👉Paper arxiv.org/pdf/2407.08711
👉Project https://omninocs.github.io/
👉Data github.com/google-deepmind/omninocs
🔥 940+ FPS Multi-Person Pose Estimation 🔥
👉RTMW (Real-Time Multi-person Whole-body pose estimation models) is a series of high-performance models for 2D/3D whole-body pose estimation. Impressive 940+ FPS on #GPU. Code & models available💙
👉Review https://t.ly/XkBmg
👉Paper arxiv.org/pdf/2407.08634
👉Repo github.com/open-mmlab/mmpose/tree/main/projects/rtmpose
🍾TAPVid-3D: benchmark for TAP-3D🍾
👉#Deepmind (+College London & Oxford) introduces TAPVid-3D, a new benchmark for evaluating long-range Tracking Any Point in 3D: 4,000+ real-world videos, composed of three different data sources spanning a variety of object types, motion patterns, and indoor/outdoor environments. Data & Code available, Apache 2.0💙
👉Review https://t.ly/SsptD
👉Paper https://arxiv.org/pdf/2407.05921
👉Project https://tapvid3d.github.io/
👉Code github.com/google-deepmind/tapnet/tree/main/tapnet/tapvid3d
🐸 Tracking Everything via Decomposition 🐸
👉Hefei unveils a novel decoupled representation that divides static scenes and dynamic objects in terms of motion and appearance. A more robust tracking through occlusions and deformations. Source Code announced under MIT License💙
👉Review https://t.ly/OsFTO
👉Paper https://arxiv.org/pdf/2407.06531
👉Repo github.com/qianduoduolr/DecoMotion
🤖 CODERS: Stereo Detection, 6D & Shape 🤖
👉CODERS: one-stage approach for Category-level Object Detection, pose Estimation and Reconstruction from Stereo images. Source Code announced💙
👉Review https://t.ly/Xpizz
👉Paper https://lnkd.in/dr5ZxC46
👉Repo (TBA)
🔥 Segment Any 4D Gaussians 🔥
👉SA4G is a novel framework to segment anything in #4D Gaussians world. HQ segmentation within seconds in 4D Gaussians and remove, recolor, compose, and render HQ anything masks. Source Code available within August 2024💙
👉Review https://t.ly/uw3FS
👉Paper https://arxiv.org/pdf/2407.04504
👉Project https://jsxzs.github.io/sa4d/
👉Repo https://github.com/hustvl/SA4D
🪴 CAVIS: SOTA Context-Aware Segmentation🪴
👉DGIST unveils the Context-Aware Video Instance Segmentation (CAVIS), a novel framework designed to enhance instance association by integrating contextual information adjacent to each object. It's the new SOTA in several benchmarks. Source Code announced💙
👉Review https://t.ly/G5obN
👉Paper arxiv.org/pdf/2407.03010
👉Repo github.com/Seung-Hun-Lee/CAVIS
👉Project seung-hun-lee.github.io/projects/CAVIS
🪩 MimicMotion: HQ Motion Generation 🪩
👉#Tencent opens a novel controllable video generation framework, dubbed MimicMotion, which can generate HQ videos of arbitrary length mimicking specific motion guidance. Source Code available💙
👉Review https://t.ly/XFoin
👉Paper arxiv.org/pdf/2406.19680
👉Project https://lnkd.in/eW-CMg_C
👉Code https://lnkd.in/eZ6SC2bc
🪅🪅Anomaly Object-Detection🪅🪅
👉The University of Edinburgh introduces a novel anomaly detection problem that focuses on identifying ‘odd-looking’ objects relative to the other instances within a multiple-views scene. Code announced💙
👉Review https://t.ly/3dGHp
👉Paper arxiv.org/pdf/2406.20099
👉Repo https://lnkd.in/d9x6FpUq
🔥 Depth Anything v2 is out! 🔥
👉 Depth Anything V2: outperforming V1 in robustness and fine-grained details. Trained from 595K synthetic labeled images and 62M+ real unlabeled images, the new SOTA in monocular depth estimation (MDE). Code & Models available💙
👉Review https://t.ly/QX9Nu
👉Paper arxiv.org/pdf/2406.09414
👉Project depth-anything-v2.github.io/
👉Repo github.com/DepthAnything/Depth-Anything-V2
👉Data huggingface.co/datasets/depth-anything/DA-2K
🌾 LLaNA: a NeRF-LLM assistant 🌾
👉UniBO unveils LLaNA; novel Multimodal-LLM that understands and reasons on an input NeRF. It processes directly the NeRF weights and performs tasks such as captioning, Q&A, & zero-shot classification of NeRFs.
👉Review https://t.ly/JAfhV
👉Paper arxiv.org/pdf/2406.11840
👉Project andreamaduzzi.github.io/llana/
👉Code & Data coming
🌮 MeshAnything with Transformers 🌮
👉MeshAnything converts any 3D representation into Artist-Created Meshes (AMs), i.e., meshes created by human artists. It can be combined with various 3D asset production pipelines, such as 3D reconstruction and generation, to transform their results into AMs that can be seamlessly applied in the 3D industry. Source Code available💙
#artificialintelligence #machinelearning #ml #AI #deeplearning #computervision #AIwithPapers #metaverse
👉Review https://t.ly/HvkD4
👉Paper arxiv.org/pdf/2406.10163
👉Code github.com/buaacyw/MeshAnythinghttps://t.ly/HvkD4
🍦Geometry Guided Depth Estimation🍦
👉A novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.18387
👉Repo (empty) https://github.com/nianticlabs/DoubleTake
🍦Geometry Guided Depth Estimation🍦
👉#Niantic (+ULC) unveils a novel system for depth estimation and #3D reconstruction which can take as input, where available, previously-made estimates of the scene’s geometry. Source Code announced💙
👉Review https://lnkd.in/dMgakzWm
👉Paper https://arxiv.org/pdf/2406.18387
👉Repo (empty) https://github.com/nianticlabs/DoubleTake
